Miscellany: A month ago, I was still talking about it Agents set up a job platform to make money, but people are too busy and not doing it (J people's quick work is the primary productive force). Now there is a platform that integrates "learning" and a vertical "order taking". It is true that 2026 is AI day, and humanity has been a hundred years To get back to the point, EvoMap is an open platform that enables the screening, sharing, and inheritance of excellent skills/successful experiences within the agent community. The underlying layer is through the GEP protocol: validated evolutionary assets (capsules/genes) and a complete ecosystem (evolution, survival, scoring, governance auditing, market matching buying and selling, etc.). The general process during runtime is as follows: 1. The Evolver engine of the local agent detects an issue ->generates Gene (policy template)+Capsule (validation package). 2. Upload the PULISH message from GEP to EvoMap Hub. EvoMap is rated using GDI (Global Desirability Index) (quality 35%+usage 30%+social 20%+freshness 15%). 4. Other agents search and inherit through FETCH messages, and report the usage effect → natural selection (good gene propagation, poor gene elimination) If the scale effect of Evomap really runs, then I think MCP, Skills, and GEP can be used as metaphors and analogies for human scenarios as follows: Goal: Henry wants to make the best brownie in the world ， -MCP taught him how to use kitchen utensils, such as how to use a standard mixer, filter, grammage reader, etc -Skills enable him to make brownies, such as knowing the detailed standard steps for chocolate to melt in water and how to make milk foam -GEP gave him the ability to make the best food: for example, he could learn exclusive skills that have been verified countless times by countless excellent bakers around the world, such as "shaking the baking tray halfway through baking to make the texture more solid“ Overall, there are no issues with the approach and implementation, but I still feel that there are quite a few problems and potential risks in the future 1. Malicious Capsule Spread Poisoning: OpenClaw has proven that malicious skills can be massively infected through the 'setup command'. Once the GEP verification mechanism is bypassed in the early stages (with few nodes and self verification loops), toxic capsules with backdoors/deleted data can expose tens of thousands of agents simultaneously. Analogous to the outbreak of biological viruses when the immune system is immature. 2. Privacy and IP Leakage During the serialization process, hard coded sensitive data (such as specific tokens, internal URLs, user PII information) can easily be packaged into GEP as part of the "success path". Due to the unstructured nature of LLM processing, traditional desensitization tools are difficult to fully cover complex semantic logic 3. Cross model distribution offset The logical weights of gene fragments that are finely adjusted on the source model (such as GPT-5) may result in bias on the target model (such as Llama-4). In a distributed A2A environment, this may manifest as unpredictable drift in the output distribution of the agent, leading to call failures. 4. Evolutionary collapse and local optima If the future really scales up, most agents will tend to download a very small number of "high scoring genes", and the system will fall into local optima. This not only undermines the generalization ability for specific long tail scenarios, but also leads to a lack of robustness for the entire Agent network when facing unknown system risks, resulting in collective failure. For example, in a trading scenario (you product, you refine) 5. Platform governance scoring issue Low quality/outdated capsules may also achieve high GDI, as "65% of the score comes from signals that are easily manipulated by short-term hype and lagging feedback"+the Beta ecosystem is not mature. The official design was ultimately eliminated through natural selection (REPORT), but in the early stages, it was a dangerous window of "fast virus transmission and slow immunity (negative feedback)". 6. The problem of economic spiral The value of Credit depends on the scale of the ecosystem, and currently, the invite wall leads to fewer contributors, which may become a "ghost city" or be targeted by volume boosting attacks. Of course, if there are more calls and contributions in the future, it may be possible to have a positive loop 7. Ethics and Responsibility Attribution: Who is responsible for causing losses when an agent uses' Inherit Capsule '? contributor? EvoMap protocol? Or end users? There are also long-standing issues such as decentralized protocol regulation and OpenClaw ecosystem fragmentation. However, EvoMap has just been launched for a few days and is still in beta. Let's explore its future development together. Finally, I would like to say that regarding the 'agents biome', my ideal ultimate state would be to reach Pluribus in the AwithA community (referring to Michael Kurland's science fiction novel from 1975, which was aired on AppleTV last year). Although Evomap is just the first step, it looks like a great start.